Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning.

Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.

Registration of fluorescein angiography and optical coherence tomography images of curved retina via scanning laser ophthalmoscopy photographs.

Accurate and automatic registration of multimodal retinal images such as fluorescein angiography (FA) and optical coherence tomography (OCT) enables utilization of supplementary information. FA is a gold standard imaging modality that depicts neurovascular structure of retina and is used for diagnosing neurovascular-related diseases such as diabetic retinopathy (DR). Unlike FA, OCT is non-invasive retinal imaging modality that provides cross-sectional data of retina. Due to differences in contrast, resolution and brightness of multimodal retinal images, the images resulted from vessel extraction of image pairs are not exactly the same. Also, prevalent feature detection, extraction and matching schemes do not result in perfect matches. In addition, the relationships between retinal image pairs are usually modeled by affine transformation, which cannot generate accurate alignments due to the non-planar retina surface. In this paper, a precise registration scheme is proposed to align FA and OCT images via scanning laser ophthalmoscopy (SLO) photographs as intermediate images. For this purpose, first a retinal vessel segmentation is applied to extract main blood vessels from the FA and SLO images. Next, a novel global registration is proposed based on the Gaussian model for curved surface of retina. For doing so, first a global rigid transformation is applied to FA vessel-map image using a new feature-based method to align it with SLO vessel-map photograph, in a way that outlier matched features resulted from not-perfect vessel segmentation are completely eliminated. After that, the transformed image is globally registered again considering Gaussian model for curved surface of retina to improve the precision of the previous step. Eventually a local non-rigid transformation is exploited to register two images perfectly. The experimental results indicate the presented scheme is more precise compared to other registration methods.

Intravenous Acetaminophen vs. Ketorolac in Terms of Pain Management in Prehospital Emergency Services: A Randomized Clinical Trial.

INTRODUCTION: Although pain management in EDs has been fully addressed in clinical trials, prehospital settings have rarely been investigated. OBJECTIVE: The present study was conducted to compare the effectiveness of intravenous acetaminophen with that of ketorolac in pre-hospital pain control. METHOD: This randomized clinical trial (RCT) was performed at a prehospital setting during EMS missions in Tehran, Iran. The eligible candidates comprised all patients over the age of 7 years with a complaint of moderate to severe pain. The patients were randomly assigned to two groups, one receiving 30 mg of intravenous (IV) ketorolac and the other 1 g of IV acetaminophen. The pain intensity was measured using a visual analog scale (VAS) before administering the analgesic and upon admission to the ED. RESULTS: The present study was conducted on 150 patients aged 8-81 years with a mean age of 40.4 ± 17.7, including 84 (56%) males. The mean reduction in the pain score was 14.9±8.6 in the acetaminophen group and 16.0±8.8 in the ketorolac group. Univariate analyses suggested no statistically significant differences between the two groups in terms of delta pain score (pain reduction) (P=0.429). CONCLUSION: Based on the obtained findings, both ketorolac and acetaminophen could be administered for pain management in prehospital settings in both traumatic and non-traumatic patients in case their contraindications are considered.